Predictive Analytics Intermediate

Predictive analytics uses historical data and ML models to forecast future network behavior. This enables proactive capacity planning, failure prevention, and intelligent alerting.

Time-Series Forecasting

Network metrics are inherently time-series data. Forecasting models capture trends, seasonality, and patterns to predict future values.

Model	Strengths	Best For
ARIMA	Statistical, well-understood, no training needed	Single metric forecasting with clear trends
Prophet	Handles seasonality, holidays, missing data	Capacity planning with business-cycle patterns
LSTM	Learns complex temporal dependencies	Multi-variate forecasting with many features
Holt-Winters	Simple, effective for seasonal data	Short-term bandwidth forecasting

Capacity Prediction

Predict when network links or devices will reach capacity using trend analysis:

Python

from prophet import Prophet
import pandas as pd

# Load 12 months of interface utilization data
df = pd.read_csv('interface_util.csv')
df.columns = ['ds', 'y']  # Prophet format

# Add capacity ceiling
df['cap'] = 100  # 100% utilization is the maximum

model = Prophet(growth='logistic', yearly_seasonality=True)
model.fit(df)

# Predict 90 days ahead
future = model.make_future_dataframe(periods=90)
future['cap'] = 100
forecast = model.predict(future)

# Find when 80% threshold is breached
breach = forecast[forecast['yhat'] >= 80].iloc[0]
print(f"Capacity threshold reached: {breach['ds']}")

Failure Probability Modeling

Predict the likelihood of device failure or link degradation using survival analysis and classification models. Key predictive features include device age, error rate trends, environmental factors, and firmware version.

Proactive Alerting

Shift Left: Instead of alerting when a threshold is breached, alert when the model predicts the threshold will be breached in the next N hours. This gives operations teams time to respond before users are affected.

Next Step

Learn the best practices for scaling analytics pipelines and building data-driven network teams.

Next: Best Practices →

← Visualization Best Practices →